Ondevice AI: How to Improve Latency and Accuracy of Neu

本文介绍了如何使用TensorFlow Lite在手机上构建一个轻量级的对象检测模型,涵盖数据集创建、MobileNets和SSD算法原理、模型训练与优化,以及在Android上的集成和效果展示。通过量化、修剪和迁移学习等技术,实现了低延迟和高精度的目标检测。

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作者:禅与计算机程序设计艺术

1.简介

On-device AI (ODA) refers to artificial intelligence technologies that are implemented within the device itself rather than using a cloud computing platform or a dedicated machine learning cluster for training and inference purposes. One key benefit of ODA is its reduced latency and energy consumption compared with traditional cloud solutions. However, despite its potential benefits, implementing an effective ODA solution can be challenging as it requires expertise in computer vision, machine learning, embedded systems development, mobile application development, and networking. In this article, we will discuss how to build an efficient and accurate object detection model directly on smartphones using only open sour

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